Since spectral data is significantly higher-dimensional than colorimetric data, the choice of operating in a spectral domain brings memory, storage and computational throughput hits with it. While spectral compression techniques exist, e.g., on the basis of Multivariate Analysis (mainly Principal Component Analysis and related methods), they result in representations of spectra that no longer have a direct physical meaning in that their individual values no longer directly express properties at a specific wavelength interval. As a result, such compressed spectral data is not suitable for direct application of physically meaningful computation and analysis. The framework presented here is an evolution and extension of the spectral correlation profile published before. It is a simple model, driven by a few adjustable parameters, that allows for the generation of nearly arbitrary, but physically realistic, spectra that can be computed efficiently, and are useful over a wide range of conditions. A practical application of its principles then includes a spectral compression approach that relies on discarding spectral wavelengths that are most redundant, given correlation to their neighbors. The goodness of representing realistic spectra is evaluated using the MIPE metric as applied to the SOCS and other databases as a reference. The end result is an efficient, yet physically meaningful, compressed spectral representation that benefits computation, transmission and storage of spectral content.